ICML page with recording/slides: https://icml.cc/virtual/2023/workshop/21499

Accepted papers: OpenReview


Oral presentations

Neural Distributed Compressor Does Binning. Ezgi Ozyilkan, Johannes Ballé, Elza Erkip

Entropy Coding of Unordered Data Structures. Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend

Neural Image Compression: Generalization, Robustness, and Spectral Bias. Kelsey Lieberman, James Diffenderfer, Charles Godfrey, Bhavya Kailkhura

Slicing Mutual Information Generalization Bounds for Neural Networks. Kimia Nadjahi, Kristjan Greenewald, Rickard Brüel Gabrielsson, Justin Solomon 🏆 Best paper award🏆

Spotlight presentations

Estimating the Rate-Distortion Function by Wasserstein Gradient Descent. Yibo Yang, Stephan Eckstein, Marcel Nutz, Stephan Mandt

Lossy Image Compression with Conditional Diffusion Model. Ruihan Yang, Stephan Mandt

NNCodec: An Open Source Software Implementation of the Neural Network Coding ISO/IEC Standard. Daniel Becking, Paul Haase, Heiner Kirchhoffer, Karsten Müller, Wojciech Samek, Detlev Marpe

On the Choice of Perception Loss Function for Learned Video Compression. Buu Phan, Sadaf Salehkalaibar, Jun Chen, Wei Yu, Ashish J Khisti

Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations. Yang Sui, Zhuohang Li, Ding Ding, Xiang Pan, Xiaozhong Xu, Shan Liu, Zhenzhong Chen

Call for Papers

The workshop solicits original research on the topic of learning-based approaches to data compression and communication.

The ubiquity of communication technology has made efficient and effective data compression an increasingly critical research area. Recent work building on deep generative models such as variational autoencoders, GANs, normalizing flows, and diffusion models has shown that machine-learning-based compression methods can significantly outperform state-of-the-art classical compression codecs for image and video data. However, there are still open questions and practical problems holding back these methods from making a major impact in real-world applications.

This workshop aims to address these issues by bringing together researchers from diverse fields including deep generative modeling, information theory, and statistics (Bayesian or otherwise). We believe that a multi-disciplinary approach to compression will lead to new synergies and result in a new generation of learnable codecs, as well as a better understanding of theoretical aspects.

Topics of interest include, but are not limited to,

  • Data compression (e.g., images, video, audio) with machine learning
  • Probabilistic modeling and inference for compression
  • Quantization, entropy coding, and stochastic coding
  • Theoretic understanding of learned compression
  • Fundamental performance bounds/limits
  • Learning-based approaches to information theory
  • Computationally-efficient models/methods
  • Perceptual metrics and image quality assessment algorithms

Important Dates

  • Submission deadline: May 27 11:59 PM AOE (anywhere on earth), 2023
  • Notification date: June 19, 2023
  • Workshop date: 9 AM - 5 PM Hawaii time (HST), July 29, 2023

Submission Instructions

Submission website: OpenReview

We solicit short workshop paper submissions of up to 4 pages + unlimited references/appendices. Please format submissions in ICML style. Submissions will be double blind: reviewers cannot see author names when conducting reviews, and authors cannot see reviewer names.

Some accepted papers will be accepted as contributed talks. All accepted posters are expected to be presented in-person at the poster session, and all papers published via Openreview after the workshop.

This workshop will not have formal proceedings, so we welcome the submission of work currently under review at other archival ML venues. We also welcome the submission of work recently published in information theory venues (e.g. Transactions on Information Theory, ISIT, ITW) that may be of interest to an ML audience. However, we will not consider work recently published in or accepted to other archival ML venues (e.g. ICML main conference).


Johannes Ballé
Research Scientist, Google
José Miguel Hernández-Lobato
Professor, Cambridge
Hyeji Kim
Assistant Professor, UT Austin
Yan Lu
Partner Research Manager, Microsoft Research Asia
Aaron Wagner
Professor, Cornell
Tsachy Weissman
Professor, Stanford


Ashish Khisti
Professor, University of Toronto
Ties van Rozendaal
Senior Deep Learning Researcher, Qualcomm
George Toderici
Senior Staff Research Scientist, Google
Rashmi Vinayak
Assistant Professor, CMU


Berivan Isik
PhD Student, Stanford
Yibo Yang
PhD Student, UC Irvine
Daniel Severo
PhD Student, University of Toronto and Vector Institute for AI
Karen Ullrich
Research Scientist, Meta AI
Robert Bamler
Professor, University of Tübingen
Stephan Mandt
Associate Professor, UC Irvine

Organizers affiliations